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PubMed2001

A context noise model of episodic word recognition.

Item noise models of recognition assert that interference at retrieval is generated by the words from the study list. Context noise models of recognition assert that interference at retrieval is generated by the contexts in which the test word has appeared. The authors introduce the bind cue decide model of episodic memory, a Bayesian context noise model, and demonstrate how it can account for data from the item noise and dual-processing approaches to recognition memory. From the item noise perspective, list strength and list length effects, the mirror effect for word frequency and concreteness, and the effects of the similarity of other words in a list are considered. From the dual-processing perspective, process dissociation data on the effects of length, temporal separation of lists, strength, and diagnosticity of context are examined. The authors conclude that the context noise approach to recognition is a viable alternative to existing approaches.

Attention↗
PubMed1998

Broadening the Tests of Learning Models.

For many years psychological studies of the learning process have used a simulated medical diagnosis task in which symptom configurations are probabilistically related to diseases. Participants are given a set of symptoms and asked to indicate which disease is present, and feedback is given on each trial. We enrich this standard laboratory task in four different ways. First, the symptoms have four possible values (low, medium low, medium high, and high) rather than just two. Second, symptom configurations are generated from an expanded factorial design rather than a simple factorial design. Third, subjects are asked to make a continuous judgment indicating their confidence in the diagnosis, rather than simply a binary judgment. Fourth, cumulated performance scores, payoffs, and the availability of a historical summary of the outcomes are varied in order to assess how these treatments modulate performance. These enrichments provide a broader data set and more challenging tests of the models. Using 123 subjects each in 480 trials, we compare five existing learning models plus several variants, including the well-known Bayesian, fuzzy logic, connectionist, exemplar, and ALCOVE models. We find that the subjects do learn to distinguish the symptom configurations, that subjects are quite heterogeneous in their response to the task, and that only a small part of the variation across subjects arises from the differences in treatments. The most striking finding is that the model that best predicts subjects' behavior is a simple Bayesian model with a single fitted parameter for prior precision to capture individual differences. We use rolling regression techniques to elucidate the behavior of this model over time and find some evidence of over-response to current stimuli. Copyright 1998 Academic Press.

Journal Article↗
PubMed2002

A model-based method for identifying species hybrids using multilocus genetic data.

We present a statistical method for identifying species hybrids using data on multiple, unlinked markers. The method does not require that allele frequencies be known in the parental species nor that separate, pure samples of the parental species be available. The method is suitable for both markers with fixed allelic differences between the species and markers without fixed differences. The probability model used is one in which parentals and various classes of hybrids (F(1)'s, F(2)'s, and various backcrosses) form a mixture from which the sample is drawn. Using the framework of Bayesian model-based clustering allows us to compute, by Markov chain Monte Carlo, the posterior probability that each individual belongs to each of the distinct hybrid classes. We demonstrate the method on allozyme data from two species of hybridizing trout, as well as on two simulated data sets.

Animals↗
PubMed2001

Bayesian 2-D deconvolution: effect of using spatially invariant ultrasound point spread functions.

Observed ultrasound images are degraded representations of the-true tissue reflectance. The specular reflections at boundaries between regions of different tissue types are blurred, and the diffuse scattering within homogenous regions causes speckle because of the oscillating nature of the transmitted pulse. To reduce both blur and speckle, we have developed algorithms for the restoration of simulated and real ultrasound images based on Markov random field models and Bayesian statistical methods. The algorithm is summarized here, although a more detailed description can be found in our companion paper [1]. Because the point spread function (psf) is unknown, we investigate the effects of using incorrect frequencies and sizes for the model psf during the restoration process. First, we degrade the images either with a known simulated psf or a measured psf. Then, we use different psf shapes during restoration to study the robustness of the method. We found that small variations in the parameters characterizing the psf, less than +/- 25% change in frequency, width, or length, still yielded satisfactory results. When altering the psf more than this, the restorations were not acceptable. The restorations were particularly sensitive to large increases in the restoring psf frequency. Thus, 2-D Bayesian restoration using a fixed psf may yield acceptable results as long as the true variant psfs have not varied too much during imaging.

Bayes Theorem↗
PubMed1999

Prediction of the international normalized ratio and maintenance dose during the initiation of warfarin therapy.

AIMS: A pharmacokinetic/pharmacodynamic model, with Bayesian parameter estimation, was used to retrospectively predict the daily International Normalized Ratios (INRs) and the maintenance doses during the initiation of warfarin therapy in 74 inpatients. METHODS: INRs and maintenance doses predicted by the model were compared with the actual INRs and the eventual maintenance dose. Cases with drugs or medical conditions interacting with warfarin or receiving concurrent heparin therapy were not excluded. As the study was retrospective, model predictions of the maintenance dose were not those that were administered. Mean prediction error (MPE) and percentage absolute prediction errors (PAPE) were used to assess the model predictions. RESULTS: INR MPE ranged from -0.07 to 0.06 and median PAPE from 10% to 20%. Dose MPE ranged from -0.7 to 0.17 mg and median PAPE from 16.7% to 37.5%. Accurate and precise dose predictions were obtained after 3 or more INR feedback's. CONCLUSIONS: This study shows that the model can accurately predict daily INRs and the maintenance dose in this sample of cases. The model can be incorporated into computer decision-support systems for warfarin therapy and may lead to improvement in the initiation of warfarin therapy.

Adult↗
PubMed2002

Extended-interval dosing of tobramycin in neonates: implications for therapeutic drug monitoring.

OBJECTIVE: Our objective was to individualize tobramycin dosing regimens in neonates of various gestational ages with use of early therapeutic drug monitoring. METHODS: This study was performed in neonatal patients with suspected septicemia in the first week of life. All patients received tobramycin, 4 mg/kg per dose, as a 30-minute intravenous infusion, with a gestational age-related initial interval of 48 hours (<32 weeks), 36 hours (32-36 weeks), and 24 hours (> or =37 weeks). The target serum peak and trough serum concentrations were 5 to 10 mg/L and 0.5 mg/L, respectively. Serum trough samples and 1- and 6-hour samples were taken after the first dose. Tobramycin concentrations were used to obtain gestational age-dependent population models with nonparametric expectation maximization software. To investigate the effect of timing of sampling in a second group of patients, serum trough samples and 3- and 8-hour samples were taken after the first dose of tobramycin was administered. Serum trough concentrations were predicted by use of linear pharmacokinetics in both groups and by use of the population models with bayesian feedback of 1 or 2 serum concentrations in the second group. These predicted concentrations were compared with actual serum trough concentrations. The predictive performance of the 1- to 6-hour and 3- to 8-hour models and the population models were compared with a gestational age-related model without therapeutic drug monitoring. RESULTS: A total of 247 patients were analyzed: 206 with 1- to 6-hour serum samples and 41 with 3- to 8-hour serum samples. Peak serum concentrations were above 5 mg/L in 90.8% of cases, and trough serum concentrations were above 1 mg/L in 25.5% of cases. The 3- to 8-hour linear model had a bias of -0.31 mg/L and a precision of 0.48 mg/L, and it performed significantly better than the 1- to 6-hour model. The best nonparametric expectation maximization model had a bias of -0.11 mg/L and a precision of 0.45 mg/L. None of the models yielded a significant improvement of predictive performance over the model without therapeutic drug monitoring. CONCLUSIONS: Routine early therapeutic drug monitoring does not improve the model-based prediction of initial tobramycin dosing intervals in neonates in the first week of life.

Anti-Bacterial Agents↗
PubMed1992

Empirical Bayes versus fully Bayesian analysis of geographical variation in disease risk.

This paper reviews methods for mapping geographical variation in disease incidence and mortality. Recent results in Bayesian hierarchical modelling of relative risk are discussed. Two approaches to relative risk estimation, along with the related computational procedures, are described and compared. The first is an empirical Bayes approach that uses a technique of penalized log-likelihood maximization; the second approach is fully Bayesian, and uses an innovative stochastic simulation technique called the Gibbs sampler. We chose to map geographical variation in breast cancer and Hodgkin's disease mortality as observed in all the health care districts of Sardinia, to illustrate relevant problems, methods and techniques.

Bayes Theorem↗
PubMedDate not supplied

Bayesian analysis of a dose-response experiment with serial sacrifices.

This paper presents analysis and comments which are believed to be appropriate for certain carcinogenesis studies where sacrifices are performed throughout the experiment. Estimates of the risk probability for each dose level and sacrifice time are found utilizing the sample likelihood as the posterior density. The dose-response relationship is investigated with these estimates as the response. In order to test if the dose is effective and to check the appropriateness of the time-to-incidence model a Bayesian multiple comparisons technique is introduced.

Animals↗
PubMedDate not supplied

How much quality control is enough? A cost-effectiveness model for clinical laboratory quality control procedures (illustrated by its application to a ligand-assay-based screening program).

Quality assurance testing represents a substantial proportion of the clinical laboratory budget, but current guidelines are based on criteria that pertain to analytic error rather than to optimization of the cost-effectiveness of patient care. A general Bayesian mathematical model for the cost-effectiveness of assay quality control has been developed, and is demonstrated using previously published data. The cost-effectiveness of quality assurance as defined here depends upon the prevalence of disease, the shapes of the distributions of test results observed in the non-diseased and diseased populations, the decision limit selected for labeling results positive or negative, the costs and benefits associated with each of the possible therapeutic outcomes, the magnitude of random and systematic analytical errors, the statistical power of the quality control test in use, the costs associated with delays due to re-assay, and the proportion of total test cost attributable to quality control procedures. Given current clinical laboratory practice, much of this information will not be routinely available. The model combines these factors into a simple equation with three terms: one for the cost of the original and any required repeat laboratory analyses, one for the cost of delay entailed by the rejection of an assay batch, and one for the change in total costs consequent to rejection of erroneous assay results.

Clinical Laboratory Techniques↗
PubMed2001

Comparison of two methods to obtain a desired first isepamicin peak in intensive care patients.

A randomized multicenter study in intensive care unit (ICU) patients, evaluated the capacity of a Bayesian method to obtain an optimal first isepamicin (ISP) peak of 80 mg/L in comparison to a fixed loading dose (LD). Patients (n=236) over 18 years of age were enrolled from 6 September 1997 to 17 July 1999 and randomly assigned to received ISP in a calculated dose (CD) or a loading dose (LD) of 25 mg/kg body weight. The CD was estimated using a specific population model with Bayesian methodology implemented in the PKS program (Abbott PKS, Abbott Diagnostics, Rungis, France). The data required included age, body weight, height, gender and serum creatinine. ISP disposition is described by a one-compartment model. Blood samples were drawn 1 and 24 h after the start of infusion for fluorescence polarization immunoassay measurement of serum ISP concentrations. The predictive performance was assessed by computing bias and precision. Peak concentrations were significantly higher in CD group than the LD group (84.2 +/- 28.6 vs. 74.7 +/- 24.1 mg/L, respectively; P=0.008), but trough levels were comparable. The optimal ISP peak was attained by a significantly higher percentage of CD patients (P=0.018), and by significantly more CD patients on mechanical ventilation (P=0.025), and with simplified acute physiological scores (SAPS) > 35 (P=0.002). Pharmacokinetic parameters were similar for the two groups with large interindividual variations. Mean (+/- SD) volume of distribution of ventilated patients (72%) was significantly higher than of nonventilated patients (23.31 +/- 7.35 vs. 20.60 +/- 6.30 L, respectively; P=0.001). No relationship was found between the volume of distribution and SAPS. Total clearance was significantly correlated with estimated CLCR (creatinine clearance) (P=0.0001). Precision (RMSE) is better for CD than for LD strategy, respectively 27.96 and 28.66 mg/L. The Bayesian method was significantly more accurate and performed particularly well in ventilated patients and patients with high SAPS, compare to an LD of 25 mg/kg to obtain a first ISP peak of 80 mg/L in ICU patients. Therefore, a fixed dose of 28.5 mg/kg would be also adequate to reach a peak of 80 mg/L.

Acute Disease↗
PubMed2000

Back-calculating the age-specific incidence of recurrent subclinical Haemophilus influenzae type b infection.

We consider the estimation of an age-specific incidence rate of a subclinical Haemophilus influenzae type b (Hib) infection from data recording the ages of children with a clinical Hib infection (Hib disease). The model is based on the assumption that the probability of being immune to clinical infection is determined by the time of the previous immunization caused by a subclinical infection, and by the distribution of the duration of immunity. We use a non-parametric Bayesian intensity model to arrive at smooth estimates of incidence rates. The estimated age-specific incidence rate of subclinical Hib infection is almost constant which indicates that the observed age-specific pattern of clinical Hib infection incidence is mainly due to immunity by either maternally derived antibodies or by immunizing subclinical infections. The estimated rate is relatively high, corresponding to one immunizing subclinical infection in less than two years.

Age Distribution↗
PubMed2003

A hybrid Bayesian-neural network approach for probabilistic modeling of bacterial growth/no-growth interface.

A hybrid probabilistic modeling approach that integrates artificial neural networks (ANNs) with statistical Bayesian conditional probability estimation is proposed. The suggested approach benefits from the power of ANNs as highly flexible nonlinear mapping paradigms, and the Bayes' theorem for computing probabilities of bacterial growth with the aid of Parzen's probability distribution function estimators derived for growth and no-growth (G/NG) states. The proposed modeling approach produces models that can predict the probability of growth of targeted microorganism as affected by a set of parameters pertaining to extrinsic factors and operating conditions. The models also can be used to define the probabilistic boundary (interface) between growth and no-growth, and as such can define and predict the values of critical parameters required to keep a desired pre-specified bacterial growth risk in check. A modular system incorporating the various computational modules was constructed to illustrate the application of the hybrid approach to the probabilistic modeling of growth of pathogenic Escherichia coli strain as affected by temperature and water activity. The proposed approach was compared to other techniques including the traditional linear and nonlinear logistic regression. Results indicated that the hybrid approach outperforms the other approaches in its accuracy as well as flexibility to extract the implicit interrelationships between the various parameters. Advantages and limitations of the approach were also discussed and compared to those of other techniques.

Bayes Theorem↗
PubMed1993

Can pharmacokinetic dosing decrease nephrotoxicity associated with aminoglycoside therapy.

A randomized, controlled clinical trial was performed to determine whether individualized dosing by use of Bayesian pharmacokinetic modeling could decrease nephrotoxicity accosted with aminoglycoside therapy. Two hundred forty-three patients receiving aminoglycosides for suspected or proven infection were randomly assigned to one of three groups: usual physician-directed dosing (Group 1), pharmacist-assisted dosing (Group 2), or pharmacist-directed dosing (Group 3). Dosing in Groups 2 and 3 was based on a Bayesian pharmacokinetic dosing program, whereas Group 1 served as the control group. Individualized dosing resulted in higher mean postinfusion (peak) serum aminoglycoside levels, higher ratios of mean peak level to minimum inhibitory concentration (peak/MIC ratios), and a trend toward lower trough serum levels. Milligrams per dose were higher and number of doses per day was lower in the pharmacist-dosed groups. However, the incidence of nephrotoxicity (> or = 100% increase in serum creatinine) was not different among the three groups (16, 27, and 16% in Groups 1, 2, and 3, respectively). Similarly, severity of toxicity was not affected by the dosing intervention. Risk factors for toxicity included duration of therapy, shock, treatment with furosemide, older age, and liver disease. After controlling for these factors, the dosing intervention still had no effect on nephrotoxicity. It was concluded that Bayesian pharmacokinetic dosing did not decrease the risk of nephrotoxicity associated with aminoglycoside therapy.

Aged↗
PubMed2001

Population pharmacokinetic modelling of carbamazepine by using the iterative Bayesian (IT2B) and the nonparametric EM (NPEM) algorithms: implications for dosage.

OBJECTIVE: To estimate individual and population postinduction pharmacokinetics of carbamazepine (CBZ) in epileptic adult and paediatric patients who received chronic CBZ monotherapy. METHODS: We have used the USC*PACK collection of PC programs for the estimations. The preinduction CBZ metabolism was also estimated in 16 volunteers after a single dose of CBZ (200 mg). We used a linear one-compartmental model with oral absorption and found the pharmacokinetic parameter values of CBZ behaviour to be in good agreement with those reported earlier. RESULTS: Serum CBZ concentrations correlated poorly with daily doses in both the adult and child populations. Because of the diversity within the population, use of the mean population model without knowledge of an individual patient's pharmacokinetic characteristics gives poor prediction. In contrast, the individual Bayesian posterior models gave good prediction for all subjects in the population, due to the removal of the interindividual variability. CONCLUSION: This approach permits one to individualize drug therapy for patients even when only sparse therapeutic drug monitoring (TDM) data are available. Future individual CBZ serum level predictions were acceptable from a clinical point of view (mean absolute error = 13.2 +/- 9.7%). The optimal sampling strategy approach helped to design an optimal cost-effective TDM protocol for CBZ therapy management.

Adult↗
PubMed2025

Generative model for the first cell fate bifurcation in mammalian development.

The first cell fate bifurcation in mammalian development directs cells toward either the trophectoderm (TE) or inner cell mass (ICM) compartments in pre-implantation embryos. This decision is regulated by the subcellular localization of a transcriptional co-activator YAP and takes place over several progressively asynchronous cleavage divisions. As a result of this asynchrony and variable arrangement of blastomeres, reconstructing the dynamics of the TE/ICM cell specification from fixed embryos is extremely challenging. To address this, we developed a live-imaging approach and applied it to measure pairwise dynamics of nuclear YAP and its direct target genes, CDX2 and SOX2, which are key transcription factors of the TE and ICM, respectively. Using these datasets, we constructed a generative model of the first cell fate bifurcation, which reveals the time-dependent statistics of the TE and ICM cell allocation. In addition to making testable predictions for the joint dynamics of the full YAP/CDX2/SOX2 motif, the model revealed the stochastic nature of the induction timing of the key cell fate determinants and identified the features of YAP dynamics that are necessary or sufficient for this induction. Notably, temporal heterogeneity was particularly prominent for SOX2 expression among ICM cells. As heterogeneities within the ICM have been linked to the initiation of the second cell fate decision in the embryo, understanding the origins of this variability is of key significance. The presented approach reveals the dynamics of the first cell fate choice and lays the groundwork for dissecting the next cell fate decisions in mouse development.

Animals↗
PubMed1991

Estimation of relative potency with sequential dilution errors in radioimmunoassay.

Sequential dilution is a very common procedure in radioimmunoassay, in which the dilution error will be accumulated from the highest to the lowest concentration. A simulated example in relative potency determination is used to demonstrate the potentially wrong conclusion that can be drawn, when the dilution error is not properly included in the model. A Bayesian method is used and an alternative approximation via maximum likelihood is proposed. An alternative experimental design is recommended to increase the precision of the inference.

Bayes Theorem↗
PubMed2000

Modeling markers of disease progression by a hidden Markov process: application to characterizing CD4 cell decline.

Multistate models have been increasingly used to model natural history of many diseases as well as to characterize the follow-up of patients under varied clinical protocols. This modeling allows describing disease evolution, estimating the transition rates, and evaluating the therapy effects on progression. In many cases, the staging is defined on the basis of a discretization of the values of continuous markers (CD4 cell count for HIV application) that are subject to great variability due mainly to short time-scale noise (intraindividual variability) and measurement errors. This led us to formulate a Bayesian hierarchical model where, at a first level, a disease process (Markov model on the true states, which are unobserved) is introduced and, at a second level, the measurement process making the link between the true states and the observed marker values is modeled. This hierarchical formulation allows joint estimation of the parameters of both processes. Estimation of the quantities of interest is performed via stochastic algorithms of the family of Markov chain Monte Carlo methods. The flexibility of this approach is illustrated by analyzing the CD4 data on HIV patients of the Concorde clinical trial.

Algorithms↗
PubMed1993

A probabilistic rule-based expert system.

This paper explores a medical expert system combining techniques of Bayesian network modelling with ideas of weighted inference rules. The weights of the individual rules can be estimated objectively from a training set of actual cases; and they can be used in a Monte Carlo stimulation to estimate objectively conditional probabilities of diagnosis given particular combinations of symptoms. The paper describes and evaluates a medical expert system built according to this design. The diagnostic accuracy of the program was found to be similar to that obtained through the usual application of Bayes theorem with the assumption of conditional independence of symptoms given disease, even though the Bayesian classifier has more than 70 times as many numerical parameters. The method may be promising in cases where small training sets do not permit accurate estimation of large numbers of parameters.

Abdominal Pain↗